fuzzy c-means also showed excellent volumetric segmentation accuracy for the total brain volume (BV) and CSF, but poorer discrimination between WM and GM. Although the remaining methods may be employed for specific tasks, their overall volumetric accuracy are rather limited. The main reason for the superiority of the adaptive Bayesian method is likely because of its incorporation of a Markov random field model that increases the robustness of the algorithm to noise.

Table 2 depicts the results for various techniques in segmenting a 3D double-echo (spin density/T2-weighted) phantom (in-plane resolution = 0.94 mm2, slice thickness = 4.5 mm, Gaussian noise with a = 9.0, 3D linear shading 7% in each direction). The adaptive fuzzy c-means technique presented the best overall accuracy, whereas the adaptive Bayesian showed good volumetric accuracy for segmentation of the total BV and total intracranial volume (ICV). In general, because of heavy partial volume effects present on double-echo images, segmentation of the total BV into WM and GM yields large errors for all techniques, with the most accurate technique being the adaptive fuzzy c-means (results not shown). Not surprisingly, these results showed that for global volumetric measurements in the brain, the Tl-weighted segmentations offered much greater accuracy.

Implementation issues such as speed, algorithm complexity, amount of manual interaction, and robustness were not directly compared here, but may be inferred from the methods' descriptions and their references. Unsupervised methods are clearly favorable in terms of reducing manual interaction and increasing reliability.